ON THE CENTRALIZATION OF WASTEWATER TREATMENT FACILITIES1

ABSTRACT. Two factors affecting the centralization of wastewater treatment facilities were investigated; the cost of collection and treatment systems and the performance of treatment plants. Based on computer-generated minimum cost designs, wastewater collection networks were found to be characterized by diseconomies of scale of magnitude similar to the reported economies of scale for wastewater treatment works. The combined costs of collection and treatment are U-shaped functions from which the least cost size of collection and treatment systems were found for particular values of population density. Examination of the day-to-day performance of five metropoitan-area waste-water treatment plants revealed that, for time series shorter than one month, the day-to-day variation in effluent quality was random, although the variation in quantity discharged was distinctly non-random. The performances of all five plants on any given day showed little correlation. This suggests that the decentralization of treatment facilities can produce benefits both through the reduction in quantities of waste discharged at a given point and through in-stream averaging of the varying performances of several treatment plants. Since the cost function of collection and treatment combined is generally flat in the region of the minimum-cost size, little penalty is invoked to gain the potential benefits of treatment plant decentralization.     

EFFICIENCY OF HYDROLOGIC DATA COLLECTION SYSTEMS ROLE OF TYPE I AND II ERRORS1

ABSTRACT The efficiency of hydrologic data collection systems is relevant to solution of environmental problems, scientific understanding of hydrologic processes, model-building and management of water resources. Because these goals may be overlapping and non-commensurate, design of data networks is not simple. Identified are four elements of error or risk in such networks: (a) choice of variables and mathematical model for the same process, (b) accuracy of model parameter estimates, (c) acceptance of wrong hypothesis or rejection of correct hypothesis and (d) economic losses associated with error. Of these four, the classical hypothesis testing problem is specifically evaluated in terms of costs of type I and II errors for simple and composite hypotheses; mathematical models for these economic analyses also include costs of sample data and costs of waiting while new data is obtained. An illustrative computational example focuses on the hypothesis that natural recharge might be augmented by a system of pumping wells along an ephemeral channel. The relationship of the hypothesis testing problem to Bayesian decision theory is discussed; it is felt that the latter theory offers a more comprehensive framework for design and use of hydrologic data networks.     

Belief Networks: A Framework for the Participatory Development of Natural Resource Management Strategies

Successful long-term management of natural resources requires the development of strategies which recognise the constraints pertaining to the environmental system and can therefore be realistically expected to work. This paper presents a new technique for strategy formulation using belief networks, a framework based on Bayesian probability calculus that supports the investigation of complex environmental systems by the user and analysis of all constraints. While mathematical in nature, belief networks are superficially simple and allow concepts to be expressed in terms with which a wide range of user will be familiar. This offers a participatory approach to the development of management strategies through consideration of the impact of potential management options with consequent benefits for strategy implementation.     

基于频率下降率的结构损伤自适应神经网络识别

笔者探讨了动量系数和学习率自适应调整的神经网络算法及结构裂纹损伤识别特征参数的选取,提出以反映结构损伤位置和程度的频率下降率作为结构裂纹损伤识别的特征参数,利用有限元网格细化法对结构裂纹损伤进行数值模拟,获取训练样本数据,通过自适应神经网络对结构裂纹损伤问题进行识别研究。从结构裂纹损伤识别实例的结果中可以看出,采用频率下降率和自适应神经网络技术对结构裂纹进行损伤识别分析具有较高的精度和可靠性。     

A FAST RECURSIVE GIS ALGORITHM FOR COMPUTING STRAHLER STREAM ORDER IN BRAIDED AND NONBRAIDED NETWORKS1

ABSTRACT: Stream ordering is a useful property of every river network, having a wide range of applications. A method for determining stream orders that quickly and easily addresses various network topologies and magnitudes is therefore needed. This paper introduces a general recursive stream ordering framework for vector hydrography. It also presents a linear, O(n), stream ordering procedure for braided river networks, which is a major improvement to the existing quadratic, O(n²), procedure. The discussion includes results and interpretations, and the appendices present procedure pseudocodes and thorough line by line explanations.     

基于车速的交通事故贝叶斯预测

为了降低交通事故的发生率 ,提高道路交通安全水平 ,提出了基于车速的贝叶斯预测方法来检测和预测交通事故。首先对车速与交通事故之间的关系进行分析。在分析的基础上 ,以车速为衡量对象 ,提出贝叶斯预测方法。通过使用车速观测数据 ,应用 χ2 检验 ,确定是否为异常数据 ;并通过最小风险的贝叶斯预测 ,确定该异常是否会导致交通事故。最后 ,绘出利用该贝叶斯预测方法进行交通事故预测的流程图     

SAMPLE UNCERTAINTY IN FLOOD LEVEE DESIGN: BAYESIAN VERSUS NON-BAYESIAN METHODS1

ABSTRACT: Bayesian and non-Bayesian flood levee design methods that account for the uncertainty due to limited record length are compared using a case study. The first method, Bayesian decision theory (BDT), imbeds the uncertainty in the parameters of the yearly peak stage into a loss function. The optimum design of the flood levee, called Bayes design, corresponds to the minimum expected loss, called Bayes risk. The second method, induced safety algorithm (ISA), computes a margin of safety to be added to either an existing levee or a levee designed by classical benefit-cost analysis. The design decision is shown to fluctuate as different record lengths are considered. For short record lengths, BDT, which takes small sample bias into account, appears to yield a more conservative design than ISA. On the other hand, ISA, which is simple to implement, seems to be preferable to BDT for longer record lengths.     

Experiences from early stages of a national industrial symbiosis programme in the UK: determinants and coordination challenges

This paper reviews the factors influencing the development and sustained operation of regional industrial symbiosis (IS) networks and discusses the roles a coordination body can play to alter these factors so as to catalyse the development and functioning of such networks. These are analysed within the context of experiences gained in the early stages of three regional IS programmes under development in the UK, and of the recently launched national IS programme (NISP) that they are part of. It is stated that the policy framework in the UK has elements supportive of such networks to evolve, and the regional public bodies are favouring their development. Based on differences among studied cases, it is argued that the nature of companies’ operations and industrial history in the regions, the extent of peer pressure, the positioning of the coordinating body in the region, and its approach to awareness raising and recruitment have major influence on the progress of the programmes. Finally, the paper emphasises the importance of aligning the characteristics of emerging operations with the longer-term sustainability requirements. If accepted, this challenge assigns additional responsibilities to the coordinating parties.     

A Markov random field spatio-temporal analysis of ocean temperature

The National Oceanic Data Center (NODC) contains historical records from approximately 144,000 hydrographic stations in the North Atlantic. This data has been used by oceanographers to construct maps of point estimates of pressure, temperature, salinity and oxygen in the North Atlantic (Levitus (1994); Lozier et al. (1995)). Because data from any particular year are scarce, the previous maps have been for time-averaged values only. In addition, the maps have been reported without uncertainty estimates. This paper presents a Markov random field (MRF) analysis that can generate maps for specific time periods along with associated uncertainties. To estimate changes in oceanic properties over time previous oceanographic work has focused on differences between a few time periods each having many observations. Due to data scarcity this poses a severe restriction for both spatial and temporal coverage of climatic change. The MRF analysis provides a means for temporal modeling that does not require high data density at each time period. To demonstrate the usefulness of a MRF analysis of oceanic data we investigate the temporal variability along 24.5°N in the North Atlantic. Our results are compared to an earlier analysis (Parrilla et al. (1994)) where data from only three time periods was used. We obtain a more thorough understanding of the temperature change found by this previous study.     

Estimating the risk of insect species invasion: Kohonen self-organising maps versus k-means clustering

Previous work on the estimation of the invasiveness of insect pest species used a single Kohonen self-organising map (SOM) to quantify the invasion potential of each member of a set of species in relation to a particular geographic region. In this paper that method is critically compared to an alternative approach of calculating the invasive potential of insect pest species as an outcome of clustering of regional species assemblages. Data clustering was performed using SOM and k-means optimisation clustering and multiple trials were performed with each algorithm. The outcomes of these two approaches were evaluated and compared to the previously published results obtained from a single SOM. The results show firstly, due to the inherent variation between trials of the algorithms used, that multiple trials are necessary to determine reliable risk ratings, and secondly, that k-means clustering can be considered a more appropriate algorithm for this particular application, as it produces clusters of higher quality, as determined by objective cluster measures, and is far more computationally efficient than SOM.